Statin adherence is lower in primary than secondary prevention: A national follow-up study of new users

Sigglekow, Finn; Horsburgh, Simon; Parkin, Lianne

doi:10.1371/journal.pone.0242424

Cited by 22 publications

(15 citation statements)

References 38 publications

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“…LDL-C reduction with statins has been a popular therapy for CVD prevention because statin treatment leads to reduced serum levels of total cholesterol, LDL-C, and triacylglycerols [27,28]. The significance of statins was recognized in the primary and secondary prevention of atherosclerotic CVDs [28][29][30][31][32][33]. In primary prevention, statins substantially decrease CVD morbidity, but only moderately reduce CVD mortality [27][28][29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

“…The significance of statins was recognized in the primary and secondary prevention of atherosclerotic CVDs [28][29][30][31][32][33]. In primary prevention, statins substantially decrease CVD morbidity, but only moderately reduce CVD mortality [27][28][29][30][31][32][33]. Statin use for primary prevention can be effective for individuals at high risk of CVD [28].…”

Section: Discussionmentioning

confidence: 99%

“…Statin use for primary prevention can be effective for individuals at high risk of CVD [28]. The beneficial effects of statins in secondary prevention were reported to reduce all-cause, CVD mortality, and cardiovascular events [29,[31][32][33].…”

Section: Discussionmentioning

confidence: 99%

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A Novel Statin Compound from Monacolin J Produced Using CYP102A1-Catalyzed Regioselective C-Hydroxylation

et al. 2021

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Statins inhibit the 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMG-CoA reductase), which is the rate-limiting enzyme in cholesterol biosynthesis. Statin therapy reduces morbidity and mortality in those who are at high risk of cardiovascular disease. Monacolin J is a statin compound, which is an intermediate in the lovastatin biosynthesis pathway, in the fungus Aspergillus terreus. It is also found in red yeast rice, which is made by culturing rice with the yeast Monascus purpureus. Monacolin J has a hydroxyl substituent at position C’-8 of monacolin L. Here, a new statin derivative from monacolin J was made through the catalysis of CYP102A1 from Bacillus megaterium. A set of CYP102A1 mutants of monacolin J hydroxylation with high catalytic activity was screened. The major hydroxylated product was C-6’a-hydroxymethyl monacolin J, whose structure was confirmed using LC–MS and NMR analysis. The C-6’a-hydroxymethyl monacolin J has never been reported before. It showed a greater ability to inhibit HMG-CoA reductase than the monacolin J substrate itself. Human liver microsomes and human CYP3A4 also showed the ability to catalyze monacolin J in producing the same product of the CYP102A1-catalyzed reaction. This result motivates a new strategy for the development of a lead for the enzymatic and chemical processes to develop statin drug candidates.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Novel Statin Compound from Monacolin J Produced Using CYP102A1-Catalyzed Regioselective C-Hydroxylation

et al. 2021

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“…Greatest success in maintaining statin therapy occurs in patients with established atherosclerotic disease. 39 Moreover, these patients are commonly followed by specialists who are well informed in the details of statin therapy.…”

Section: For Statin Intolerance In Patients With Clinical Atherosclerotic Disease Maximize Ldl Loweringmentioning

confidence: 99%

Statin Intolerance and Noncompliance: An Empiric Approach

Grundy

Vega

2022

The American Journal of Medicine

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“…In New Zealand, adherence to statin over the 3 years after an Acute Coronary Syndrome (ACS) was found to be 66%, although it was 82% for those on a statin prior to ACS admission [ 19 ]. In primary prevention in New Zealand, statin adherence in the first year after initiation of treatment was found to be 63% [ 20 ]. A US study found non-adherence to statin to be as high as 56.0% for secondary prevention patients and 56.4% for primary prevention patients [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Medication adherence prediction through temporal modelling in cardiovascular disease management

Hsu

Warren

Riddle

2022

BMC Med Inform Decis Mak

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Background Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and it is the leading cause of death. Like other chronic diseases, the course of cardiovascular disease is usually prolonged and its management necessarily long-term. Despite being highly effective in reducing CVD risk, non-adherence to long-term medication continues to be a longstanding challenge in healthcare delivery. The study investigates the benefits of integrating patient history and assesses the contribution of explicitly temporal models to medication adherence prediction in the context of lipid-lowering therapy. Methods Data from a CVD risk assessment tool is linked to routinely collected national and regional data sets including pharmaceutical dispensing, hospitalisation, lab test results and deaths. The study extracts a sub-cohort from 564,180 patients who had primary CVD risk assessment for analysis. Based on community pharmaceutical dispensing record, proportion of days covered (PDC) $$\ge$$ ≥ 80 is used as the threshold for adherence. Two years (8 quarters) of patient history before their CVD risk assessment is used as the observation window to predict patient adherence in the subsequent 5 years (20 quarters). The predictive performance of temporal deep learning models long short-term memory (LSTM) and simple recurrent neural networks (Simple RNN) are compared against non-temporal models multilayer perceptron (MLP), ridge classifier (RC) and logistic regression (LR). Further, the study investigates the effect of lengthening the observation window on the task of adherence prediction. Results Temporal models that use sequential data outperform non-temporal models, with LSTM producing the best predictive performance achieving a ROC AUC of 0.805. A performance gap is observed between models that can discover non-linear interactions between predictor variables and their linear counter parts, with neural network (NN) based models significantly outperforming linear models. Additionally, the predictive advantage of temporal models become more pronounced when the length of the observation window is increased. Conclusion The findings of the study provide evidence that using deep temporal models to integrate patient history in adherence prediction is advantageous. In particular, the RNN architecture LSTM significantly outperforms all other model comparators.

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Statin adherence is lower in primary than secondary prevention: A national follow-up study of new users

Cited by 22 publications

References 38 publications

A Novel Statin Compound from Monacolin J Produced Using CYP102A1-Catalyzed Regioselective C-Hydroxylation

A Novel Statin Compound from Monacolin J Produced Using CYP102A1-Catalyzed Regioselective C-Hydroxylation

Statin Intolerance and Noncompliance: An Empiric Approach

Medication adherence prediction through temporal modelling in cardiovascular disease management

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